Here are preliminary results of the bibliometric mapping of the 2022 Luxembourg research evaluation. Its purpose is:
The method for the research-field-mapping can be reviewed here:
Note: The analysis in this document depicts the larger research field around the department, thereby all analysis results are based on the publications of the department plus related
Note: The seed articles deemed representative for
the active areas of research in the institution, and include authors
affiliated with the institution. The departments research field is
identified by selecting the 2000 most similar publications to the
selected seed articles. See Technical descriptionfor
additional explanations.
| AU | PY | TI | JI |
|---|---|---|---|
| WANG J;JIN C;TANG Q;LIU Z;A… | 2022 | CRYPTOREC: NOVEL COLLABORATIVE FILTERING RECOMMENDER MADE PRIVACY-PRESERVING EASY | IEEE TRANS. DEPENDABLE SECU… |
| YILMA BA;PANETTO H;NAUDET Y | 2021 | SYSTEMIC FORMALISATION OF CYBER-PHYSICAL-SOCIAL SYSTEM (CPSS): A SYSTEMATIC LITERATURE REVIEW | COMPUT IND |
| MAYER N;AUBERT J | 2021 | A RISK MANAGEMENT FRAMEWORK FOR SECURITY AND INTEGRITY OF NETWORKS AND SERVICES | J. RISK RES. |
| GUTIERREZ-GOMEZ L;PETRY F;K… | 2020 | A COMPARISON FRAMEWORK OF MACHINE LEARNING ALGORITHMS FOR MIXED-TYPE VARIABLES DATASETS: A CASE S… | IEEE ACCESS |
| MAHJRI I;FAYE S;KHADRAOUI D | 2019 | IMPACT AND DEPLOYMENT OF DYNAMIC TRAFFIC LIGHT CONTROL STRATEGIES USING A CITY-WIDE SIMULATION SC… | IEEE INTELL. TRANSP. SYST. … |
| MCGEE F;GHONIEM M;MELANÇON … | 2019 | THE STATE OF THE ART IN MULTILAYER NETWORK VISUALIZATION | COMPUT GRAPHICS FORUM |
| MCCALL R;MCGEE F;MIRNIG A;M… | 2019 | A TAXONOMY OF AUTONOMOUS VEHICLE HANDOVER SITUATIONS | TRANSP. RES. PART A POLICY … |
| MEULEPAS JM;RONCKERS CM;SME… | 2019 | RADIATION EXPOSURE FROM PEDIATRIC CT SCANS AND SUBSEQUENT CANCER RISK IN THE NETHERLANDS | J. NATL. CANCER INST. |
Note: This section provides basic descriptives of th
identified research fielld, including number of articles over time,
countries, institutions, and authors. See
Technical descriptionfor additional explanations.
Note: Here, we report the results of a LDA
topic-modelling (basically, clustering on words) on all title+abstract
texts. Identified topics can be interpreted as broad themes in the
research field. See Technical descriptionfor additional
explanations.
Note: While this static vies is helpful, I recommend using the interactive LDAVis version to be found under https://daniel-hain.github.io/biblio_lux_2022/output/topic_modelling/LDAviz_list_itis.rds/index.html#topic=1&lambda=0.60&term=. For functionality and usage, see ´Technical ´.
Note: This analysis refers the co-citation analysis,
where the cited references and not the original publications are the
unit of analysis. Identified knowledge bases can be interpreted as the
knowledge foundation the field draws from. See
Technical descriptionfor additional explanations.
| name | dgr_int | dgr |
|---|---|---|
| Knowledge Base 1: KB 1: Complex Networks, Complex Systems (n = 1073, density =7.72) | ||
| BULDYREV S.V. PARSHANI R. PAUL G. STANLEY H.E. HAVLIN S. CATASTROPHIC CASCADE OF FAILURES IN INTERDEPENDENT NETWORKS (2010) | 4416 | 4416 |
| KIVELÄ M. ARENAS A. BARTHELEMY M. GLEESON J.P. MORENO Y. PORTER M.A. MULTILAYER NETWORKS (2014) | 2737 | 2740 |
| GAO J. BULDYREV S.V. STANLEY H.E. HAVLIN S. NETWORKS FORMED FROM INTERDEPENDENT NETWORKS (2012) | 2195 | 2198 |
| BATTISTON F. NICOSIA V. LATORA V. STRUCTURAL MEASURES FOR MULTIPLEX NETWORKS (2014) | 1502 | 1505 |
| PARSHANI R. BULDYREV S.V. HAVLIN S. INTERDEPENDENT NETWORKS: REDUCING THE COUPLING STRENGTH LEADS TO A CHANGE FROM A FIRST TO SECOND ORDER PERCOLAT… | 1331 | 1331 |
| DE DOMENICO M. NICOSIA V. ARENAS A. LATORA V. STRUCTURAL REDUCIBILITY OF MULTILAYER NETWORKS (2015) | 1324 | 1324 |
| BOCCALETTI S. THE STRUCTURE AND DYNAMICS OF MULTILAYER NETWORKS (2014) | 1160 | 1160 |
| GAO J. BULDYREV S.V. HAVLIN S. STANLEY H.E. ROBUSTNESS OF A NETWORK OF NETWORKS (2011) | 1114 | 1114 |
| DE DOMENICO M. GRANELL C. PORTER M.A. ARENAS A. THE PHYSICS OF SPREADING PROCESSES IN MULTILAYER NETWORKS (2016) | 1041 | 1041 |
| SZELL M. LAMBIOTTE R. THURNER S. MULTIRELATIONAL ORGANIZATION OF LARGE-SCALE SOCIAL NETWORKS IN AN ONLINE WORLD (2010) | 1021 | 1021 |
| Knowledge Base 2: KB 2: Human Factors in Automated Systems (n = 976, density =4.75) | ||
| PARASURAMAN R. SHERIDAN T.B. WICKENS C.D. A MODEL FOR TYPES AND LEVELS OF HUMAN INTERACTION WITH AUTOMATION (2000) | 3730 | 3739 |
| LEE J.D. SEE K.A. TRUST IN AUTOMATION: DESIGNING FOR APPROPRIATE RELIANCE (2004) | 2118 | 2118 |
| PARASURAMAN R. RILEY V. HUMANS AND AUTOMATION: USE MISUSE DISUSE ABUSE (1997) | 1523 | 1523 |
| ENDSLEY M.R. TOWARD A THEORY OF SITUATION AWARENESS IN DYNAMIC SYSTEMS (1995) | 1221 | 1221 |
| BAINBRIDGE L. IRONIES OF AUTOMATION (1983) | 1008 | 1008 |
| ENDSLEY M.R. KIRIS E.O. THE OUT-OF-THE-LOOP PERFORMANCE PROBLEM AND LEVEL OF CONTROL IN AUTOMATION (1995) | 963 | 963 |
| SHERIDAN T.B. VERPLANK W.L. (1978) | 688 | 688 |
| HART S.G. STAVELAND L.E. DEVELOPMENT OF NASA-TLX (TASK LOAD INDEX) | 606 | 606 |
| ENDSLEY M.R. SITUATION AWARENESS GLOBAL ASSESSMENT TECHNIQUE (SAGAT) | 485 | 485 |
| KABER D.B. ENDSLEY M.R. THE EFFECTS OF LEVEL OF AUTOMATION AND ADAPTIVE AUTOMATION ON HUMAN PERFORMANCE SITUATION AWARENESS AND WORKLOAD IN A DYNAM… | 474 | 474 |
| Knowledge Base 3: KB 3: Privacy Protection for Collaborative Data Analysis (n = 854, density =9.53) | ||
| DWORK C. MCSHERRY F. NISSIM K. SMITH A. CALIBRATING NOISE TO SENSITIVITY IN PRIVATE DATA ANALYSIS (2006) | 4599 | 5076 |
| DWORK C. ROTH A. THE ALGORITHMIC FOUNDATIONS OF DIFFERENTIAL PRIVACY (2014) | 4381 | 4920 |
| SHOKRI R. SHMATIKOV V. PRIVACY-PRESERVING DEEP LEARNING (2015) | 2344 | 3783 |
| DWORK C. KENTHAPADI K. MCSHERRY F. MIRONOV I. NAOR M. OUR DATA OURSELVES: PRIVACY VIA DISTRIBUTED NOISE GENERATION (2006) | 1645 | 1744 |
| CHAUDHURI K. MONTELEONI C. SARWATE A.D. DIFFERENTIALLY PRIVATE EMPIRICAL RISK MINIMIZATION (2011) | 1484 | 1589 |
| SHOKRI R. STRONATI M. SONG C. SHMATIKOV V. MEMBERSHIP INFERENCE ATTACKS AGAINST MACHINE LEARNING MODELS (2017) | 1480 | 1934 |
| MCSHERRY F. TALWAR K. MECHANISM DESIGN VIA DIFFERENTIAL PRIVACY (2007) | 1447 | 1493 |
| DWORK C. DIFFERENTIAL PRIVACY: A SURVEY OF RESULTS (2008) | 1413 | 1736 |
| FREDRIKSON M. JHA S. RISTENPART T. MODEL INVERSION ATTACKS THAT EXPLOIT CONFIDENCE INFORMATION AND BASIC COUNTERMEASURES (2015) | 1387 | 1860 |
| ABADI M. CHU A. GOODFELLOW I. MCMAHAN H.B. MIRONOV I. TALWAR K. ZHANG L. DEEP LEARNING WITH DIFFERENTIAL PRIVACY (2016) | 1355 | 1780 |
| Knowledge Base 4: KB 4: Encryption Technologies for Data Protection (n = 737, density =10.28) | ||
| GENTRY C. FULLY HOMOMORPHIC ENCRYPTION USING IDEAL LATTICES (2009) | 2152 | 2422 |
| FAN J. VERCAUTEREN F. SOMEWHAT PRACTICAL FULLY HOMOMORPHIC ENCRYPTION (2012) | 1888 | 1948 |
| PAILLIER P. PUBLIC-KEY CRYPTOSYSTEMS BASED ON COMPOSITE DEGREE RESIDUOSITY CLASSES (1999) | 1821 | 2149 |
| LIU J. JUUTI M. LU Y. ASOKAN N. OBLIVIOUS NEURAL NETWORK PREDICTIONS VIA MINIONN TRANSFORMATIONS (2017) | 1601 | 1934 |
| MOHASSEL P. ZHANG Y. SECUREML: A SYSTEM FOR SCALABLE PRIVACY-PRESERVING MACHINE LEARNING (2017) | 1517 | 2024 |
| GILAD-BACHRACH R. DOWLIN N. LAINE K. LAUTER K. NAEHRIG M. WERNSING J. CRYPTONETS: APPLYING NEURAL NETWORKS TO ENCRYPTED DATA WITH HIGH THROUGHPUT A… | 1205 | 1681 |
| BRAKERSKI Z. GENTRY C. VAIKUNTANATHAN V. (LEVELED) | 1152 | 1223 |
| BRAKERSKI Z. FULLY HOMOMORPHIC ENCRYPTION WITHOUT MODULUS SWITCHING FROM CLASSICAL GAPSVP (2012) | 1076 | 1103 |
| CHEON J.H. KIM A. KIM M. SONG Y. HOMOMORPHIC ENCRYPTION FOR ARITHMETIC OF APPROXIMATE NUMBERS (2017) | 933 | 954 |
| JUVEKAR C. VAIKUNTANATHAN V. CHANDRAKASAN A. GAZELLE: A LOW LATENCY FRAMEWORK FOR SECURE NEURAL NETWORK INFERENCE (2018) | 918 | 1048 |
| Knowledge Base 5: KB 5: Visualisation (n = 704, density =4.52) | ||
| BREHMER M. MUNZNER T. A MULTI-LEVEL TYPOLOGY OF ABSTRACT VISUALIZATION TASKS (2013) | 839 | 839 |
| LEE B. PLAISANT C. PARR C.S. FEKETE J.-D. HENRY N. TASK TAXONOMY FOR GRAPH VISUALIZATION (2006) | 587 | 587 |
| HOLTEN D. HIERARCHICAL EDGE BUNDLES: VISUALIZATION OF ADJACENCY RELATIONS IN HIERARCHICAL DATA (2006) | 524 | 537 |
| SHNEIDERMAN B. THE EYES HAVE IT: A TASK BY DATA TYPE TAXONOMY FOR INFORMATION VISUALIZATIONS (1996) | 499 | 514 |
| GLEICHER M. ALBERS D. WALKER R. JUSUFI I. HANSEN C.D. ROBERTS J.C. VISUAL COMPARISON FOR INFORMATION VISUALIZATION (2011) | 406 | 406 |
| HOLTEN D. VAN WIJK J.J. FORCE-DIRECTED EDGE BUNDLING FOR GRAPH VISUALIZATION (2009) | 363 | 363 |
| MUNZNER T. A NESTED MODEL FOR VISUALIZATION DESIGN AND VALIDATION (2009) | 353 | 353 |
| MUNZNER T. (2014) | 328 | 328 |
| BECK F. BURCH M. DIEHL S. WEISKOPF D. THE STATE OF THE ART IN VISUALIZING DYNAMIC GRAPHS (2014) | 305 | 308 |
| BURCH M. VEHLOW C. BECK F. DIEHL S. WEISKOPF D. PARALLEL EDGE SPLATTING FOR SCALABLE DYNAMIC GRAPH VISUALIZATION (2011) | 249 | 249 |
| Knowledge Base 6: KB 6: Machine Learning (n = 534, density =9.15) | ||
| BREIMAN L. RANDOM FORESTS (2001) | 3748 | 3914 |
| FRIEDMAN J.H. GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE (2001) | 2018 | 2070 |
| BREIMAN L. BAGGING PREDICTORS (1996) | 934 | 974 |
| DEMŠAR J. STATISTICAL COMPARISONS OF CLASSIFIERS OVER MULTIPLE DATA SETS (2006) | 914 | 920 |
| BERGSTRA J. BENGIO Y. RANDOM SEARCH FOR HYPER-PARAMETER OPTIMIZATION (2012) | 890 | 1068 |
| CHEN T. GUESTRIN C. XGBOOST: A SCALABLE TREE BOOSTING SYSTEM (2016) | 726 | 762 |
| ALTMAN N.S. AN INTRODUCTION TO KERNEL AND NEAREST-NEIGHBOR NONPARAMETRIC REGRESSION (1992) | 621 | 639 |
| BROWN I. MUES C. AN EXPERIMENTAL COMPARISON OF CLASSIFICATION ALGORITHMS FOR IMBALANCED CREDIT SCORING DATA SETS (2012) | 615 | 615 |
| CORTES C. VAPNIK V. SUPPORT-VECTOR NETWORKS (1995) | 545 | 549 |
| GEURTS P. ERNST D. WEHENKEL L. EXTREMELY RANDOMIZED TREES (2006) | 277 | 281 |
| Knowledge Base 7: KB 7: Medical Imaging Analysis (n = 439, density =11.17) | ||
| PEARCE M.S. SALOTTI J.A. LITTLE M.P. RADIATION EXPOSURE FROM CT SCANS IN CHILDHOOD AND SUBSEQUENT RISK OF LEUKAEMIA AND BRAIN TUMOURS: A RETROSPECT… | 1553 | 1553 |
| MATHEWS J.D. FORSYTHE A.V. BRADY Z. CANCER RISK IN 680 000 PEOPLE EXPOSED TO COMPUTED TOMOGRAPHY SCANS IN CHILDHOOD OR ADOLESCENCE: DATA LINKAGE ST… | 1088 | 1088 |
| MIGLIORETTI D.L. JOHNSON E. WILLIAMS A. THE USE OF COMPUTED TOMOGRAPHY IN PEDIATRICS AND THE ASSOCIATED RADIATION EXPOSURE AND ESTIMATED CANCER RIS… | 648 | 648 |
| SIEGEL J.A. PENNINGTON C.W. SACKS B. SUBJECTING RADIOLOGIC IMAGING TO THE LINEAR NO-THRESHOLD HYPOTHESIS: A NON SEQUITUR OF NON-TRIVIAL PROPORTION … | 334 | 334 |
| OZASA K. SHIMIZU Y. SUYAMA A. STUDIES OF THE MORTALITY OF ATOMIC BOMB SURVIVORS REPORT 14 1950-2003: AN OVERVIEW OF CANCER AND NONCANCER DISEASES (… | 333 | 333 |
| SIEGEL J.A. WELSH J.S. DOES IMAGING TECHNOLOGY CAUSE CANCER? DEBUNKING THE LINEAR NO-THRESHOLD MODEL OF RADIATION CARCINOGENESIS (2016) | 318 | 318 |
| JOURNY N. REHEL J.L. DUCOU LE POINTE H. ARE THE STUDIES ON CANCER RISK FROM CT SCANS BIASED BY INDICATION? ELEMENTS OF ANSWER FROM A LARGE-SCALE CO… | 304 | 304 |
| BOUTIS K. COGOLLO W. FISCHER J. FREEDMAN S.B. BEN DAVID G. THOMAS K.E. PARENTAL KNOWLEDGE OF POTENTIAL CANCER RISKS FROM EXPOSURE TO COMPUTED TOMOG… | 293 | 293 |
| PEARCE M.S. SALOTTI J.A. LITTLE M.P. MCHUGH K. LEE C. KIM K.P. RADIATION EXPOSURE FROM CT SCANS IN CHILDHOOD AND SUBSEQUENT RISK OF LEUKAEMIA AND B… | 259 | 259 |
| CALABRESE E.J. DHAWAN G. KAPOOR R. KOZUMBO W.J. RADIOTHERAPY TREATMENT OF HUMAN INFLAMMATORY DISEASES AND CONDITIONS: OPTIMAL DOSE (2019) | 250 | 250 |
Note: This analysis refers the bibliographic
coupling analysis, where original publications are the unit of analysis.
Identified research areas can be interpreted as the field’s current
research frontier. See Technical descriptionfor additional
explanations.
| AU | PY | TI | dgr_int | TC | TC_year |
|---|---|---|---|---|---|
| Research Area 1: RA 1: Machine Learning Techniques (n = 881, density =0.72) | |||||
| CHEN T;GUESTRIN C | 2016 | XGBOOST: A SCALABLE TREE BOOSTING SYSTEM | 3.43 | 11484 | 1914.00 |
| XIA Y;LIU C;LI Y;LIU N | 2017 | A BOOSTED DECISION TREE APPROACH USING BAYESIAN HYPER-PARAMETER OPTIMIZATION FOR CREDIT SCORING | 17.41 | 347 | 69.40 |
| NAIMI B;ARAÚJO MB | 2016 | SDM: A REPRODUCIBLE AND EXTENSIBLE R PLATFORM FOR SPECIES DISTRIBUTION MODELLING | 7.75 | 315 | 52.50 |
| ZHANG C;LIU C;ZHANG X;… | 2017 | AN UP-TO-DATE COMPARISON OF STATE-OF-THE-ART CLASSIFICATION ALGORITHMS | 8.69 | 220 | 44.00 |
| XU G;WU H-Z;SHI YQ | 2016 | ENSEMBLE OF CNNS FOR STEGANALYSIS: AN EMPIRICAL STUDY | 16.39 | 104 | 17.33 |
| VRABLECOVÁ P;BOU EZZED… | 2018 | SMART GRID LOAD FORECASTING USING ONLINE SUPPORT VECTOR REGRESSION | 18.70 | 80 | 20.00 |
| BENTÉJAC C;CSÖRGŐ A;MA… | 2021 | A COMPARATIVE ANALYSIS OF GRADIENT BOOSTING ALGORITHMS | 19.46 | 76 | 76.00 |
| SHERIDAN RP;WANG WM;LI… | 2016 | EXTREME GRADIENT BOOSTING AS A METHOD FOR QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS | 7.63 | 173 | 28.83 |
| PROBST P;BOULESTEIX A-L | 2018 | TO TUNE OR NOT TO TUNE THE NUMBER OF TREES IN RANDOM FOREST | 15.67 | 84 | 21.00 |
| MANGALATHU S;JANG H;HW… | 2020 | DATA-DRIVEN MACHINE-LEARNING-BASED SEISMIC FAILURE MODE IDENTIFICATION OF REINFORCED CONCRETE SHEAR WALLS | 14.30 | 80 | 40.00 |
| Research Area 2: RA 2: Human Automation (n = 752, density =0.27) | |||||
| ENDSLEY MR | 2017 | FROM HERE TO AUTONOMY: LESSONS LEARNED FROM HUMAN-AUTOMATION RESEARCH | 4.92 | 258 | 51.60 |
| ERIKSSON A;STANTON NA | 2017 | TAKEOVER TIME IN HIGHLY AUTOMATED VEHICLES: NONCRITICAL TRANSITIONS TO AND FROM MANUAL CONTROL | 3.51 | 321 | 64.20 |
| MERCADO JE;RUPP MA;CHE… | 2016 | INTELLIGENT AGENT TRANSPARENCY IN HUMAN-AGENT TEAMING FOR MULTI-UXV MANAGEMENT | 4.52 | 147 | 24.50 |
| PAYRE W;CESTAC J;DELHO… | 2016 | FULLY AUTOMATED DRIVING: IMPACT OF TRUST AND PRACTICE ON MANUAL CONTROL RECOVERY | 5.12 | 119 | 19.83 |
| ENDSLEY MR | 2018 | AUTOMATION AND SITUATION AWARENESS | 2.22 | 260 | 65.00 |
| SCHAEFER KE;CHEN JYC;S… | 2016 | A META-ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF TRUST IN AUTOMATION: IMPLICATIONS FOR UNDERSTANDING AUTONOMY IN … | 1.99 | 284 | 47.33 |
| KABER DB | 2018 | ISSUES IN HUMAN–AUTOMATION INTERACTION MODELING: PRESUMPTIVE ASPECTS OF FRAMEWORKS OF TYPES AND LEVELS OF AUTOMATION | 8.35 | 66 | 16.50 |
| NOY IY;SHINAR D;HORREY WJ | 2018 | AUTOMATED DRIVING: SAFETY BLIND SPOTS | 5.54 | 99 | 24.75 |
| LU Z;HAPPEE R;CABRALL … | 2016 | HUMAN FACTORS OF TRANSITIONS IN AUTOMATED DRIVING: A GENERAL FRAMEWORK AND LITERATURE SURVEY | 4.73 | 108 | 18.00 |
| SHNEIDERMAN B | 2020 | HUMAN-CENTERED ARTIFICIAL INTELLIGENCE: RELIABLE, SAFE & TRUSTWORTHY | 3.10 | 154 | 77.00 |
| Research Area 3: RA 3: Privacy Protection and AI (n = 597, density =1.26) | |||||
| ABADI M;MCMAHAN HB;CHU… | 2016 | DEEP LEARNING WITH DIFFERENTIAL PRIVACY | 14.91 | 1323 | 220.50 |
| BONAWITZ K;IVANOV V;KR… | 2017 | PRACTICAL SECURE AGGREGATION FOR PRIVACY-PRESERVING MACHINE LEARNING | 10.28 | 712 | 142.40 |
| NASR M;SHOKRI R;HOUMAN… | 2019 | COMPREHENSIVE PRIVACY ANALYSIS OF DEEP LEARNING: PASSIVE AND ACTIVE WHITE-BOX INFERENCE ATTACKS AGAINST CENTRALIZED AND FE… | 18.81 | 262 | 87.33 |
| HITAJ B;ATENIESE G;PER… | 2017 | DEEP MODELS UNDER THE GAN: INFORMATION LEAKAGE FROM COLLABORATIVE DEEP LEARNING | 9.92 | 416 | 83.20 |
| PAPERNOT N;SONG S;MIRO… | 2018 | SCALABLE PRIVATE LEARNING WITH PATE | 20.04 | 164 | 41.00 |
| TRUEX S;STEINKE T;BARA… | 2019 | A HYBRID APPROACH TO PRIVACY-PRESERVING FEDERATED LEARNING | 15.55 | 139 | 46.33 |
| PAPERNOT N;GOODFELLOW … | 2017 | SEMI-SUPERVISED KNOWLEDGE TRANSFER FOR DEEP LEARNING FROM PRIVATE TRAINING DATA | 21.07 | 99 | 19.80 |
| JAYARAMAN B;EVANS D | 2019 | EVALUATING DIFFERENTIALLY PRIVATE MACHINE LEARNING IN PRACTICE | 15.13 | 110 | 36.67 |
| ZHU T;LI G;ZHOU W;YU PS | 2017 | DIFFERENTIALLY PRIVATE DATA PUBLISHING AND ANALYSIS: A SURVEY | 10.87 | 152 | 30.40 |
| YU L;LIU L;PU C;GURSOY… | 2019 | DIFFERENTIALLY PRIVATE MODEL PUBLISHING FOR DEEP LEARNING | 21.22 | 77 | 25.67 |
| Research Area 4: RA 4: Multilayer Network (n = 596, density =0.56) | |||||
| DE DOMENICO M;GRANELL … | 2016 | THE PHYSICS OF SPREADING PROCESSES IN MULTILAYER NETWORKS | 12.04 | 336 | 56.00 |
| BIANCONI G | 2018 | MULTILAYER NETWORKS: STRUCTURE AND FUNCTION | 6.34 | 175 | 43.75 |
| LIU X;STANLEY HE;GAO J | 2016 | BREAKDOWN OF INTERDEPENDENT DIRECTED NETWORKS | 10.42 | 89 | 14.83 |
| RADICCHI F;BIANCONI G | 2017 | REDUNDANT INTERDEPENDENCIES BOOST THE ROBUSTNESS OF MULTIPLEX NETWORKS | 13.22 | 58 | 11.60 |
| MAJHI S;PERC M;GHOSH D | 2016 | CHIMERA STATES IN UNCOUPLED NEURONS INDUCED BY A MULTILAYER STRUCTURE | 4.58 | 161 | 26.83 |
| DEL GENIO CI;GÓMEZ-GAR… | 2016 | SYNCHRONIZATION IN NETWORKS WITH MULTIPLE INTERACTION LAYERS | 9.22 | 79 | 13.17 |
| YUAN X;HU Y;STANLEY HE… | 2017 | ERADICATING CATASTROPHIC COLLAPSE IN INTERDEPENDENT NETWORKS VIA REINFORCED NODES | 8.65 | 77 | 15.40 |
| MAJDANDZIC A;BRAUNSTEI… | 2016 | MULTIPLE TIPPING POINTS AND OPTIMAL REPAIRING IN INTERACTING NETWORKS | 9.19 | 70 | 11.67 |
| SHEKHTMAN LM;DANZIGER … | 2016 | RECENT ADVANCES ON FAILURE AND RECOVERY IN NETWORKS OF NETWORKS | 9.13 | 69 | 11.50 |
| HACKETT A;CELLAI D;GÓM… | 2016 | BOND PERCOLATION ON MULTIPLEX NETWORKS | 10.70 | 58 | 9.67 |
| Research Area 5: RA 5: IT for Radiation Protection (n = 460, density =0.41) | |||||
| BABL FE;BORLAND ML;PHI… | 2017 | ACCURACY OF PECARN, CATCH, AND CHALICE HEAD INJURY DECISION RULES IN CHILDREN: A PROSPECTIVE COHORT STUDY | 8.53 | 147 | 29.40 |
| PATEL AP;FISHER JL;NIC… | 2019 | GLOBAL, REGIONAL, AND NATIONAL BURDEN OF BRAIN AND OTHER CNS CANCER, 1990–2016: A SYSTEMATIC ANALYSIS FOR THE GLOBAL BURDE… | 3.10 | 214 | 71.33 |
| HONG J-Y;HAN K;JUNG J-… | 2019 | ASSOCIATION OF EXPOSURE TO DIAGNOSTIC LOW-DOSE IONIZING RADIATION WITH RISK OF CANCER AMONG YOUTHS IN SOUTH KOREA | 7.06 | 64 | 21.33 |
| MOORE MM;KULAYLAT AN;H… | 2016 | MAGNETIC RESONANCE IMAGING IN PEDIATRIC APPENDICITIS: A SYSTEMATIC REVIEW | 6.19 | 57 | 9.50 |
| MEULEPAS JM;RONCKERS C… | 2019 | RADIATION EXPOSURE FROM PEDIATRIC CT SCANS AND SUBSEQUENT CANCER RISK IN THE NETHERLANDS | 2.74 | 119 | 39.67 |
| SMITH-BINDMAN R;WANG Y… | 2019 | INTERNATIONAL VARIATION IN RADIATION DOSE FOR COMPUTED TOMOGRAPHY EXAMINATIONS: PROSPECTIVE COHORT STUDY | 3.83 | 77 | 25.67 |
| BELLOLIO MF;HEIEN HC;S… | 2017 | INCREASED COMPUTED TOMOGRAPHY UTILIZATION IN THE EMERGENCY DEPARTMENT AND ITS ASSOCIATION WITH HOSPITAL ADMISSION | 4.57 | 60 | 12.00 |
| REHANI MM;YANG K;MELIC… | 2020 | PATIENTS UNDERGOING RECURRENT CT SCANS: ASSESSING THE MAGNITUDE | 3.41 | 77 | 38.50 |
| NAGAYAMA Y;ODA S;NAKAU… | 2018 | RADIATION DOSE REDUCTION AT PEDIATRIC CT: USE OF LOW TUBE VOLTAGE AND ITERATIVE RECONSTRUCTION | 5.38 | 42 | 10.50 |
| WURZEL DF;MARCHANT JM;… | 2016 | PROTRACTED BACTERIAL BRONCHITIS IN CHILDREN: NATURAL HISTORY AND RISK FACTORS FOR BRONCHIECTASIS | 2.97 | 71 | 11.83 |
| Research Area 6: RA 6: Recommender Systems (n = 440, density =0.33) | |||||
| TANG J;WANG K | 2018 | PERSONALIZED TOP-N SEQUENTIAL RECOMMENDATION VIA CONVOLUTIONAL SEQUENCE EMBEDDING | 3.89 | 488 | 122.00 |
| LI J;REN P;CHEN Z;REN … | 2017 | NEURAL ATTENTIVE SESSION-BASED RECOMMENDATION | 2.93 | 511 | 102.20 |
| YU F;LIU Q;WU S;WANG L… | 2016 | A DYNAMIC RECURRENT MODEL FOR NEXT BASKET RECOMMENDATION | 2.75 | 256 | 42.67 |
| LIN K;ZHAO R;XU Z;ZHOU J | 2018 | EFFICIENT LARGE-SCALE FLEET MANAGEMENT VIA MULTI-AGENT DEEP REINFORCEMENT LEARNING | 3.48 | 135 | 33.75 |
| MAN T;SHEN H;JIN X;CHE… | 2017 | CROSS-DOMAIN RECOMMENDATION: AN EMBEDDING AND MAPPING APPROACH | 4.03 | 116 | 23.20 |
| YU X;JIANG F;DU J;GONG D | 2019 | A CROSS-DOMAIN COLLABORATIVE FILTERING ALGORITHM WITH EXPANDING USER AND ITEM FEATURES VIA THE LATENT FACTOR SPACE OF AUXI… | 2.73 | 117 | 39.00 |
| CHAE D-K;KIM S-W;KANG … | 2018 | CFGAN: A GENERIC COLLABORATIVE FILTERING FRAMEWORK BASED ON GENERATIVE ADVERSARIAL NETWORKS | 2.59 | 98 | 24.50 |
| ZHENG Y;TANG B;DING W;… | 2016 | A NEURAL AUTOREGRESSIVE APPROACH TO COLLABORATIVE FILTERING | 3.38 | 73 | 12.17 |
| TORRADO RR;BONTRAGER P… | 2018 | DEEP REINFORCEMENT LEARNING FOR GENERAL VIDEO GAME AI | 4.20 | 51 | 12.75 |
| LI P;TUZHILIN A | 2020 | DDTCDR: DEEP DUAL TRANSFER CROSS DOMAIN RECOMMENDATION | 4.62 | 45 | 22.50 |
| Research Area 7: RA 7: Visual Analytics (n = 376, density =0.27) | |||||
| SATYANARAYAN A;MORITZ … | 2017 | VEGA-LITE: A GRAMMAR OF INTERACTIVE GRAPHICS | 1.07 | 315 | 63.00 |
| WONGSUPHASAWAT K;MORIT… | 2016 | VOYAGER: EXPLORATORY ANALYSIS VIA FACETED BROWSING OF VISUALIZATION RECOMMENDATIONS | 1.29 | 236 | 39.33 |
| BECK F;BURCH M;DIEHL S… | 2017 | A TAXONOMY AND SURVEY OF DYNAMIC GRAPH VISUALIZATION | 1.73 | 162 | 32.40 |
| GLEICHER M | 2018 | CONSIDERATIONS FOR VISUALIZING COMPARISON | 3.20 | 84 | 21.00 |
| VON LANDESBERGER T;BRO… | 2016 | MOBILITYGRAPHS: VISUAL ANALYSIS OF MASS MOBILITY DYNAMICS VIA SPATIO-TEMPORAL GRAPHS AND CLUSTERING | 1.69 | 142 | 23.67 |
| LAM H;TORY M;MUNZNER T | 2018 | BRIDGING FROM GOALS TO TASKS WITH DESIGN STUDY ANALYSIS REPORTS | 3.20 | 33 | 8.25 |
| WU Y;PITIPORNVIVAT N;Z… | 2016 | EGOSLIDER: VISUAL ANALYSIS OF EGOCENTRIC NETWORK EVOLUTION | 1.41 | 72 | 12.00 |
| SRINIVASAN A;STASKO J | 2018 | ORKO: FACILITATING MULTIMODAL INTERACTION FOR VISUAL EXPLORATION AND ANALYSIS OF NETWORKS | 1.64 | 62 | 15.50 |
| ANDRIENKO G;ANDRIENKO … | 2017 | REVEALING PATTERNS AND TRENDS OF MASS MOBILITY THROUGH SPATIAL AND TEMPORAL ABSTRACTION OF ORIGIN-DESTINATION MOVEMENT DATA | 1.46 | 68 | 13.60 |
| LIU D;WENG D;LI Y;BAO … | 2017 | SMARTADP: VISUAL ANALYTICS OF LARGE-SCALE TAXI TRAJECTORIES FOR SELECTING BILLBOARD LOCATIONS | 0.82 | 117 | 23.40 |
| Research Area 8: RA 8: Security and Privacy protection for Machine Learning tasks (n = 373, density =1.15) | |||||
| YANG Q;LIU Y;CHEN T;TO… | 2019 | FEDERATED MACHINE LEARNING: CONCEPT AND APPLICATIONS | 4.02 | 1279 | 426.33 |
| MOHASSEL P;ZHANG Y | 2017 | SECUREML: A SYSTEM FOR SCALABLE PRIVACY-PRESERVING MACHINE LEARNING | 4.37 | 589 | 117.80 |
| JUVEKAR C;VAIKUNTANATH… | 2018 | GAZELLE: A LOW LATENCY FRAMEWORK FOR SECURE NEURAL NETWORK INFERENCE | 10.61 | 222 | 55.50 |
| LIU J;JUUTI M;LU Y;ASO… | 2017 | OBLIVIOUS NEURAL NETWORK PREDICTIONS VIA MINIONN TRANSFORMATIONS | 4.93 | 240 | 48.00 |
| JIANG X;LAUTER K;KIM M… | 2018 | SECURE OUTSOURCED MATRIX COMPUTATION AND APPLICATION TO NEURAL NETWORKS | 12.70 | 82 | 20.50 |
| CHEON JH;HAN K;KIM A;K… | 2018 | BOOTSTRAPPING FOR APPROXIMATE HOMOMORPHIC ENCRYPTION | 11.83 | 74 | 18.50 |
| SADEGH RIAZI M;SONGHOR… | 2018 | CHAMELEON: A HYBRID SECURE COMPUTATION FRAMEWORK FOR MACHINE LEARNING APPLICATIONS | 6.14 | 132 | 33.00 |
| MOHASSEL P;RINDAL P | 2018 | ABY3: A MIXED PROTOCOL FRAMEWORK FOR MACHINE LEARNING | 3.77 | 199 | 49.75 |
| SADEGH RIAZI M;SAMRAGH… | 2019 | XONN: XNOR-BASED OBLIVIOUS DEEP NEURAL NETWORK INFERENCE | 9.99 | 75 | 25.00 |
| DOWLIN N;GILAD-BACHRAC… | 2017 | MANUAL FOR USING HOMOMORPHIC ENCRYPTION FOR BIOINFORMATICS: THIS PAPER PROVIDES A NEW HOMOMORPHIC ENCRYPTION ALGORITHM AND… | 10.41 | 52 | 10.40 |
All results are preliminary so far…